Systems and methods for detecting cytopathic effect in cells

ABSTRACT

A method for detecting cytopathic effect (CPE) in a well sample includes generating a well image depicting a well containing cells and a medium (and possibly viruses), and pre-processing the well image at least by partitioning the well image into sub-images each corresponding to a different portion of the well. The method also includes, for each of some or all of the sub-images, determining, by analyzing the sub-image using a convolutional neural network, a respective score indicative of a likelihood that any cells in the portion of the well corresponding to the sub-image exhibit CPE. The method further includes determining a CPE status of the cells contained in the well based on the respective scores for the sub-images, and generating output data indicating the CPE status.

FIELD OF DISCLOSURE

The present application relates generally to viral detection techniques,and more specifically to techniques for detecting cytopathic effect(CPE) in cells.

BACKGROUND

When a virus infects a host cell, the host cell may undergostructural/morphological changes referred to as “cytopathic effect,” orCPE. In some applications (e.g., when performing quality controlprocedures in connection with certain commercial drugs, or for researchand development purposes), it is necessary to inspect cell culturesamples for the presence of CPE. For example, virus stock potency istypically measured using titration assays, which are classical, cellculture-based methods that rely on visual observations of virus-inducedcytopathology. One commonly used technique for quantifying the amount ofan infectious virus is the “tissue culture infection dose 50%,” orTCID₅₀, assay. TCID₅₀ assays are endpoint dilution assays that quantifythe amount of virus required to produce CPE in 50% of inoculated tissueculture cells. TCID₅₀ assays may be used for viral clearance studies(e.g., when determining the ability of a particular purification processto remove or inactivate a virus), for example.

Conventionally, CPE is manually detected by human analysts inspectingimages of wells. For a TCID₅₀ assay, for example, a human analyst mayneed to inspect a number of well images that each correspond to adifferent dilution level. Manual visual inspection is a time consumingprocess, as the analyst must carefully inspect each image for anyevidence of CPE. Moreover, the task is complicated—and the accuracy ofCPE or non-CPE classifications can suffer—due to the fact that differentcell lines (e.g., the L929, PG4, Vero and 324K cell lines) can havedifferent morphologies when exhibiting CPE, as well as the fact thatdifferent viruses can induce different cytopathic effects in host cellsof a single cell line. Different cytopathic effects may includeelongation, inclusion bodies, foci formation, syncytia formation, and/orcell lysis, for example.

SUMMARY

Embodiments described herein relate to systems and methods that improveupon conventional visual inspection techniques used for CPE detection.In particular, a visual inspection system captures at least one digitalimage of each well within a well plate, with each well containing anumber of cells in a medium (and possibly viruses, e.g., according to acontrolled dilution). As used herein “well” refers to anylaboratory-scale cell culture environment that permits opticalinspection of its contents. While wells on multi-well plates arediscussed by way of example herein, it will be appreciated that wherevera “well” and a “well plate” are mentioned, unless stated otherwise,these terms are contemplated to encompass any suitable laboratory-scalecell culture environment permitting optical inspection of its contents.Each well image is pre-processed by partitioning the image into a numberof segments, or “sub-images,” that each correspond to (i.e., depict) adifferent portion of the well. The well image may also be pre-processedin other ways, such as removing portions of the image that depict areasoutside of the well.

For a given well image, each sub-image is analyzed using a convolutionalneural network (CNN), in order to determine a score for that sub-image.The CNN may be specific to the cell line in the well that is beinginspected (e.g., the L929, PG4, Vero or 324K cell line). The score foreach sub-image is indicative of the likelihood that any cells in theportion of the well corresponding to the sub-image exhibit CPE. Forexample, each score may be a probability that is greater than 0.00000and less than 1.00000. Collectively, the scores for the varioussub-images may be used to determine (e.g., predict) a CPE status of thecells depicted in the entire well image. For example, the sub-imagescores may be used to determine, in binary fashion, whether the contentsof the well image, as a whole, exhibit CPE. In one such embodiment, thesub-image scores are input to a support vector machine (SVM) thatclassifies the contents depicted in the well image as “CPE” or “not CPE”(or another, similar binary classification). In other embodiments, theCPE status is not binary. For example, the sub-image scores may be usedto determine a probability that the contents of the well image, as awhole, exhibit CPE, such as a probability at or below a specifiedthreshold, for example no more than a 50%, 40%, 30%, 20%, 10%, 5%, 3%,2%, 1%, or 0.1% probability that the contents of the well image, as awhole, exhibit CPE. Optionally, a well may be determined to be “not CPR”or to have a probability that the content of the well image, as a whole,exhibits CPE below the specified threshold can be selected for furthercell culture. For example, a cell of the well (as a single cell, orcomprised by a portion of the contents of the well) can be transferredto a new culture environment, and cultured in the new cultureenvironment. Information on cell culture can be found, for example, inGreen and Sambrook, “Molecular Cloning: A Laboratory Manual” (4thedition) Cold Spring Harbor Laboratory Press 2012, which is incorporatedby reference herein in its entirety.

The CPE status may be determined at each of a number of serial dilutionstages (e.g., for a TCID₅₀ assay), in some embodiments. Depending on theapplication, the CPE status (e.g., classification), or CPE statuses atdifferent stages of serial dilution, may be used in different ways. Forexample, a graphical user interface (GUI) may present the CPE status orstatuses to a human user. As another example, the CPE status(es) may beprovided to another software application or computer system, e.g., forthe purpose of gathering statistics across many wells and/or wellplates. The CPE status(es), and/or statistics that take the CPE statusesof a number of wells or well plates into account, may be used todetermine the capacity of a purification process to remove or inactivatea virus, for example (e.g., for a quality control procedure during drugmanufacture, or for research and development purposes, etc.), or for anyother suitable purpose.

BRIEF DESCRIPTION OF THE DRAWINGS

The skilled artisan will understand that the figures, described herein,are included for purposes of illustration and do not limit the presentdisclosure. The drawings are not necessarily to scale, and emphasis isinstead placed upon illustrating the principles of the presentdisclosure. It is to be understood that, in some instances, variousaspects of the described implementations may be shown exaggerated orenlarged to facilitate an understanding of the describedimplementations. In the drawings, like reference characters throughoutthe various drawings generally refer to functionally similar and/orstructurally similar components.

FIG. 1 is a simplified block diagram of an example system that mayimplement the techniques described herein.

FIG. 2 depicts an example visual inspection system that may be used inthe system as described herein, such as the system of FIG. 1 .

FIG. 3 depicts example images of various well samples of different celllines, with and without CPE.

FIG. 4 depicts an example of a partitioned well image.

FIG. 5 is a flow diagram of an example method for detecting cytopathiceffect (CPE) in a well sample.

DETAILED DESCRIPTION

The various concepts introduced above and discussed in greater detailbelow may be implemented in any of numerous ways, and the describedconcepts are not limited to any particular manner of implementation.Examples of implementations are provided for illustrative purposes.

FIG. 1 is a simplified block diagram of an example system 100 that mayimplement the techniques described herein. System 100 includes a visualinspection system 102 communicatively coupled to a computer system 104.Visual inspection system 102 includes hardware (e.g., a well platestage, one or more lenses and/or mirrors, an imager, etc.), as well asfirmware and/or software, that is configured to capture digital imagesof wells within a well plate. While FIG. 1 depicts, and is primarilydescribed herein with reference to, an embodiment in which visualinspection system 102 is controlled by computer system 104, it isunderstood that, in other embodiments, visual inspection system 102 maypurely (or primarily) implement local control (e.g., if visualinspection system 102 includes an off-the-shelf product such as theCloneSelect imager from Molecular Devices, LLC).

An example embodiment of visual inspection system 102 is shown in FIG. 2. As seen in FIG. 2 , visual inspection system 102 may include a stage202 that is configured to receive a well plate 204 containing a numberof wells (not shown in FIG. 2 ). Well plate 204 may be any suitable sizeand/or shape, and have any suitable number of wells disposed thereon(e.g., 6, 24, 96, 384, 1536, etc.). Moreover, the wells may be arrangedin any suitable pattern on well plate 204, such as a 2:3 rectangularmatrix, for example.

Visual inspection system 102 further includes an illumination system208, and an imager 210 that is configured to acquire images.Illumination system 208 may include any suitable number and/or type(s)of light source(s) configured to generate source light, and illuminateseach well of well plate 204 when that well is positioned in the opticalpath of imager 210. In various embodiments, each well may have one ormore transparent and/or opaque portions. For example, each of the wellsmay be entirely transparent, or may have transparent bottoms with theside walls being opaque. Each of the wells may generally be cylindrical,or have any other suitable shape (e.g., a cube, etc.).

Visual inspection system 102 may image each of the wells in well plate204 sequentially. To this end, visual inspection system 102 may beconfigured to move imager 210, and/or one or more optical elements(e.g., mirrors) that adjust the optical path of imager 210, so as tosuccessively align each of the wells with the optical path of imager 210for individual well analysis. Alternatively, visual inspection system102 may move stage 202 along one or more (e.g., x and/or y) axes tosuccessively align the different wells. Imager 210, stage 202, and/orother components of visual inspection system 102 may be coupled to oneor more motorized actuators, for example. Regardless of which mechanismis used to align different wells with the optical path of imager 210, aseach well is aligned imager 210 acquires one or more images of theilluminated well.

It is understood that FIG. 2 shows only one example embodiment of visualinspection system 102, and that others are possible. For example, visualinspection system 102 may include multiple imagers similar to imager 210(e.g., for three-dimensional imaging), illumination system 208 mayinstead be configured to provide backlighting for well plate 204, and soon. Moreover, while not shown in FIG. 2 , visual inspection system 102may include one or more communication interfaces and processors toenable communication with computer system 104, and/or one or moreprocessors to provide local control of certain operations (e.g.,capturing images by imager 210, if not controlled by computer system104).

Referring again now to FIG. 1 , computer system 104 may, in thisembodiment, generally be configured to control/automate the operation ofvisual inspection system 102, and to receive and process imagescaptured/generated by visual inspection system 102, as discussed furtherbelow. Computer system 104 is also coupled to a training server 106 viaa network 108. Network 108 may be a single communication network, or mayinclude multiple communication networks of one or more types (e.g., oneor more wired and/or wireless local area networks (LANs), and/or one ormore wired and/or wireless wide area networks (WANs) such as theInternet). As discussed further herein, training server 106 is generallyconfigured to train one or more machine learning (ML) models 109, whichtraining server 106 sends to computer system 104 via network 108. Invarious embodiments, training server 106 may provide ML model(s) 109 asa “cloud” service (e.g., Amazon Web Services), or training server 106may be a local server. Alternatively or additionally, ML model(s) 109is/are transferred to computer system 104 by a technique other than aremote download (e.g., by physically transferring a portable storagedevice to computer system 104), in which case system 100 may not includenetwork 108. In some embodiments, one, some or all of ML model(s) 109may be trained on computer system 104, and then uploaded to server 106.In other embodiments, computer system 104 performs the model traininglocally without uploading the ML model(s) 109 to training server 106, inwhich case system 100 may omit both network 108 and training server 106.As yet another example, system 100 may include a cloud computingenvironment in which training server 106 (or another server that is notshown in FIG. 1 but is communicatively coupled to computer system 104via network 108) performs the scoring, classification, and/or otheroperations discussed below in connection with computer system 104. Insome embodiments, some or all of the components of computer system 104shown in FIG. 1 (e.g., one, some or all of modules 120 through 126) areinstead included in visual inspection system 102, in which case visualinspection system 102 may communicate directly with training server 106via network 108.

Computer system 104 may be a general-purpose computer that isspecifically programmed to perform the operations discussed herein, ormay be a special-purpose computing device. As seen in FIG. 1 , computersystem 104 includes a processing unit 110, a network interface 112 and amemory unit 114. In some embodiments, however, computer system 104includes two or more computers that are either co-located or remote fromeach other. In these distributed embodiments, the operations describedherein relating to processing unit 110, network interface 112 and/ormemory unit 114 may be divided among multiple processing units, networkinterfaces and/or memory units, respectively.

Processing unit 110 includes one or more processors, each of which maybe a programmable microprocessor that executes software instructionsstored in memory 114 to execute some or all of the functions of computersystem 104 as described herein. Processing unit 110 may include one ormore graphics processing units (GPUs) and/or one or more centralprocessing units (CPUs), for example. Alternatively, or in addition,some of the processors in processing unit 110 may be other types ofprocessors (e.g., application-specific integrated circuits (ASICs),field-programmable gate arrays (FPGAs), etc.), and some of thefunctionality of computer system 104 as described herein may instead beimplemented in hardware. Network interface 112 may include any suitablehardware (e.g., a front-end transmitter and receiver hardware),firmware, and/or software configured to communicate with training server106 via network 108 using one or more communication protocols. Forexample, network interface 112 may be or include an Ethernet interface,enabling computer system 104 to communicate with training server 106over the Internet or an intranet, etc. Memory unit 114 may include oneor more volatile and/or non-volatile memories. Any suitable memory typeor types may be included, such as read-only memory (ROM), random accessmemory (RAM), flash memory, a solid-state drive (SSD), a hard disk drive(HDD), and so on. Collectively, memory unit 114 may store one or moresoftware applications, the data received/used by those applications, andthe data output/generated by those applications.

Memory unit 114 stores the software instructions of a CPE detectionapplication 118 that, when executed by processing unit 110, determines aCPE status for the contents of a well based on a well image. Whilevarious modules of application 118 are discussed below, it is understoodthat those modules may be distributed among different softwareapplications, and/or that the functionality of any one such module maybe divided among different software applications.

In some embodiments, a visual inspection system (VIS) control module 120of application 118 controls/automates operation of visual inspectionsystem 102, via commands or other messages, such that images of thesamples within the wells of well plate 204 in FIG. 2 can be generatedwith little or no human interaction. Visual inspection system 102 maysend the images to computer system 104 for storage in memory unit 114,or another suitable memory not shown in FIG. 1 . The operation of VIScontrol module 120 is discussed in further detail below. As noted above,however, visual inspection system 102 may not be externally controlledin certain embodiments, in which case VIS control module 120 may haveless functionality than is described herein (e.g., only handling theretrieval of images from visual inspection system 102), or may beomitted entirely from application 118.

An image pre-processing module 122 of application 118 performs one ormore operations to prepare a given well image for further processing. Inparticular, image pre-processing module 122 partitions a well image intoa number of sub-images, and may perform one or more other tasks (e.g.,removing portions of the well image that do not depict any contents ofthe well, and/or processing the well image to enhance contrast and/orremove noise, etc.). The sub-images may be square images, rectangularimages, or have some other suitable shape (e.g., a pie slice shape thatextends from the center of the well to the outer perimeter of the well).All sub-images may be of equal size, or the sizes may differ (e.g., withlarger square images near the well center, and smaller square imagesnear the outer perimeter of the well). The operation of imagepre-processing module 122 is discussed in further detail below.

A sub-image scoring module 124 of application 118 analyzes each of someor all of the sub-images generated by image pre-processing module 122using a CNN (e.g., one of ML model(s) 109). For each sub-image, the CNNoutputs a score that indicates a likelihood that the well contentsdepicted in that sub-image exhibit CPE. Thus, each score may be viewedas a confidence level associated with a positive CPE classification fora respective sub-image. The CNN may include any suitable number ofconvolutional layers for two-dimensional convolution (e.g., to detectfeatures such as edges within images), any suitable number of poolinglayers (e.g., a down-sampling layer, to reduce computation whilepreserving the relative locations of features), and any suitable numberof fully-connected layers (e.g., to provide high-level reasoning basedon features). Alternatively (e.g., if visual inspection system 102implements three-dimensional imaging techniques), the CNN of sub-imagescoring module 124 may utilize three-dimensional convolution to detectfeatures in three dimensions. The operation of sub-image scoring module124 is discussed in further detail below.

A CPE classification module 126 of application 118 analyzes thesub-image scores for a particular well image, and outputs a CPE statusfor the well image. The CPE status may be binary (e.g., “CPE” versus“not CPE”), in which case CPE classification module 126 may generate thestatus by inputting the sub-image scores to an SVM (e.g., one of MLmodel(s) 109). If scores were determined for n+1 different sub-images ofa well, for example, the SVM may classify the CPE status for the wellcontents using an n-dimensional hyperplane (e.g., a hyperplaneconstructed during training by training server 106). In otherembodiments, the CPE status is some other suitable indicator relating tothe existence of CPE, the likelihood of CPE, and/or the extent to whichCPE exists, in the entirety of the well contents. For example, the CPEstatus may be a score that indicates a likelihood that the well contentsexhibit CPE. As such, the CPE status may be expressed as a probabilitysuch as a percentage. Optionally, a determination may be made (forexample, whether the risk of CPE is sufficiently low to use the wellcontents for further cell culture) based on whether the likelihood thatthe well contents exhibit CPE falls below a specified threshold, forexample, less than or equal to 50%, 40%, 30%, 20%, 10%, 5%, 3%, 2%, 1%,or 0.1% probability that the well contents exhibited CPE. As anotherexample, the CPE status may be the percentage portion of the wellcontents (e.g., by area) that exhibits CPE. In some embodiments, CPEclassification module 126 also provides other information, such asinformation relating to the exhibited morphology (e.g., elongation, celllysis, etc.). The operation of CPE classification module 126 isdiscussed in further detail below.

Operation of system 100, according to some embodiments, will now bedescribed with reference to FIGS. 1 and 2 , and with reference to aparticular embodiment in which computer system 104 controls visualinspection system 102 and implements models trained by training server106. Initially, in this embodiment, training server 106 trains MLmodel(s) 109 using data stored in a training database 130 (e.g.,input/feature data, and corresponding labels). ML model(s) 109 includesa CNN implemented by sub-image scoring module 124, and possibly an SVMimplemented by CPE classification module 126. Training database 130 mayinclude a single database stored in a single memory (e.g., HDD, SSD,etc.), a single database distributed across multiple memories, ormultiple databases stored in one or more memories. To train the CNNimplemented by sub-image scoring module 124, training database 130 mayinclude a large number of training data sets each corresponding to asingle sub-image (e.g., with the same magnification level, size and/orother characteristics as the sub-images output by image pre-processingmodule 122), along with a label indicating a correct classification forthat sub-image (e.g., “CPE” or “not CPE”). The labels may beclassifications that were made by human analysts when reviewing thetraining sub-images. In some embodiments, the training data includesimages of a variety of cell lines, to ensure that the CNN can accuratelyscore sample sub-images across different cell lines.

Alternatively, to improve classification accuracy, ML model(s) 109 mayinclude a different CNN for each of multiple cell lines (e.g., L929,PG4, Vero, and 324K cell lines), with each CNN having been trained usingonly sub-images that depict cells of the corresponding cell line. Insuch embodiments, computer system 104 may initially obtain copies of theCNNs for all cell lines, and sub-image scoring module 124 may select andimplement the CNN corresponding to the cell line that is currently beinginspected (e.g., as indicated by a user entering the cell line via auser interface of computer system 104, not shown in FIG. 1 ).Alternatively, computer system 104 may only retrieve the CNN for thecell line currently being inspected on an as-needed basis (e.g., bysending a request, including an indication of the user-specified cellline, to training server 106). If ML model(s) 109 include an SVMimplemented by CPE classification module 126, the SVM may or may not bespecific to the cell line currently being inspected, depending on theembodiment.

In some embodiments where ML model(s) 109 include a CNN (or one CNN percell line, etc.) and an SVM (or one SVM per cell line, etc.), the SVM(s)is/are trained using outputs of the CNN(s). If ML model(s) 109 include aseparate SVM for each cell line, each SVM may be trained using outputsof the trained CNN corresponding to that same cell line. As one example,for a given cell line, a CNN may be trained using thousands of wellsub-images that were manually labeled by human analysts. Outputs(scores) generated by the CNN may then be used as inputs for trainingthe SVM, with the labels for training the SVM (e.g., “CPE” or “not CPE”for entire well images) also being provided by a human analyst, or beingdetermined automatically based on the manual labels that were assignedto the sub-images.

In some embodiments, training server 106 uses additional labeled datasets in training database 130 in order to validate the generated MLmodel(s) 109 (e.g., to confirm that a given one of ML model(s) 109provides at least some minimum acceptable accuracy). Training server 106then provides ML model(s) 109 to computer system 104 (e.g., via a remotedownload over network 108) or, in a cloud computing embodiment, eitherimplements ML model(s) 109 locally or provides ML model(s) 109 to one ormore other servers. In some embodiments, training server 106 alsoupdates/refines one or more of ML model(s) 109 on an ongoing basis. Forexample, after ML model(s) 109 are initially trained to provide asufficient level of accuracy, visual inspection system 102 or computersystem 104 may provide additional images to training server 106 overtime, and training server 106 may use supervised or unsupervisedlearning techniques to further improve the model accuracy.

Each of the wells within well plate 204 of visual inspection system 102is at least partially filled, either automatically or manually, with amedium that includes suitable nutrients for cells (e.g., amino acids,vitamins, etc.), growth factors, and/or other ingredients, and the wellis inoculated with cells of a particular cell line. As used herein, a“particular cell line” refers to a cell line having a discrete identity,such as a specified cell line. Well plate 204 is then loaded onto stage202, and VIS control module 120 causes visual inspection system 102 tomove stage 202, illumination system 208, and/or other components (e.g.,one or more mirrors) in small increments, and to activate imager 210(and possibly illumination system 208) in a synchronized manner, suchthat imager 210 captures at least one image for each of the wells inwell plate 204.

Either as well images are generated, or in batches after subsets (orall) of the images have been generated (e.g., after locally storing allimages on a hard drive), visual inspection system 102 sends the imagesto computer system 104 for automated analysis. As with the process ofcapturing well images, the process of transferring images to computersystem 104 may be automated (e.g., triggered by commands from VIScontrol module 120), in some embodiments.

The process of imaging the wells in well plate 204 may be repeated incertain embodiments and/or scenarios. For a TCID₅₀ assay, for instance,VIS control module 120 may cause visual inspection system 102 to capturean image of each well at each of a series of different dilution levels.In some embodiments, VIS control module 120 (or another module withinapplication 118) also controls/automates a system (not shown in FIG. 1 )that sets the dilution levels for the well samples.

For each of the well images received from visual inspection system 102,as noted above, image pre-processing module 122 partitions the wellimage into sub-images, and possibly performs one or more otherpre-processing operations such as removing parts of the image thatdepict areas outside of the well. Sub-image scoring module 124 then usesa CNN of ML model(s) 109 to score each sub-image, with the scoreindicating the likelihood that the well contents depicted in thesub-image exhibit CPE. The score may be a confidence level associatedwith a classification of “CPE,” for example. CPE classification module126 then analyzes all of the scores for the sub-images of that well todetermine the CPE status of the well. For example, CPE classificationmodule 126 may use an SVM of ML model(s) 109 to classify the wellcontents as “CPE” or “not CPE.” As another example, CPE classificationmodule 126 may apply one or more heuristics to classify the wellcontents (e.g., by classifying the contents of the well as “CPE” anytime that the scores for the sub-images, when added, exceed somethreshold value, or any time at least three sub-images have a score over0.5000, etc.). Regardless of how CPE classification module 126 uses thescores to classify the well contents, the process may be repeated fordifferent well images until a suitable stopping point is reached (e.g.,until images of all wells in a well plate are analyzed, or until imagesof all wells at all desired dilution levels are analyzed, etc.).

Application 118 also generates output data reflecting theclassification/status as determined by CPE classification module 126.This output data may take various forms, and be used in various ways,depending on the embodiment. For example, application 118 may cause auser interface (e.g., a GUI displayed on a screen of computer system 104or another system, not shown in FIG. 1 ) to present the output data,including the CPE status of one or more well images, to a user. Asanother example, application 118 may send the output data to anotherapplication being executed on computer system 104 (or another system notshown in FIG. 1 ), e.g., to trigger a next stage in a viral clearance orother process. As another example, application 118 may, based on thegenerated output data, cause samples within wells exhibiting CPE to bediscarded or set aside for other purposes.

FIG. 3 depicts example images of various well samples of different celllines, with and without CPE, to illustrate some of the challenges thatmay be associated with determining CPE status of a sample based on awell image if using conventional approaches. A first image pair 300shows L929 (left) and 324K (right) cell lines that do not exhibit CPE,while a second image pair 302 shows two different morphologies of theL929 cell line when exhibiting CPE. Each of the images in image pair 300and each of the images in image pair 302 may be a sub-image from alarger well image, for example.

As can be seen from the image pairs 300 and 302, L929 cells exhibitingCPE may be quite difficult to distinguish from 324K cells not exhibitingCPE. Accordingly, as noted above, sub-image scoring module 124 mayimplement a CNN that is specific to the cell line being inspected. Imagepair 302 further shows that even a single cell line can have verydifferent morphologies (e.g., when infected by different viruses). Thus,even for a single cell line, it may be beneficial for training server106 to train the CNN corresponding to that cell line using samples withdifferent morphologies. Well sub-images corresponding to differentmorphologies may be intentionally introduced into training database 130,or may simply be a result of having a suitably large database (e.g.,thousands, or tens of thousands, etc., of well sub-images).

A third image pair 304 shows, on the left, an image of an entire wellcontaining cells of the L929 cell line, and on the right, a specificsub-image corresponding to one portion of that well image. This exampleillustrates the fact that, conventionally, CPE may also be difficult todetect due to its localization within the well. In the image pair 304,for instance, CPE is exhibited as a relatively small spot within thewell. The potential localization of CPE, and/or other patterns or trendsof CPE that may occur with different cell lines and/or differentviruses, may inherently be accounted for by CPE classification module126 when analyzing the sub-image scores for a given well image. Toensure that CPE classification module 126 can handle such variations, anSVM of CPE classification module 126 may have been trained usingsub-image score arrangements that reflect different types of CPEpatterns and/or localization.

FIG. 4 depicts an example image 400 of a well 402. Well 402 may be oneof the wells in well plate 204, and/or well image 400 may be an imagethat was generated by visual inspection system 102 in response to acommand from VIS control module 120, for example. Well image 400 mayrepresent a bottom-up perspective of well 402.

Image pre-processing module 122 partitions well image 400 into a numberof sub-images 404. While FIG. 4 shows that sub-images 404 are generatedonly in areas of image 400 that depict at least a portion of well 402,in other embodiments the entire well image 400 may be partitioned intoequal-size sub-images, and/or sub-images of different sizes and/orshapes may be generated. Regardless of whether the entire well image 400is partitioned, image pre-processing module 122 may discard or ignorethe portions of well image 400 that do not depict at least a portion ofwell 402. This cropping of well image 400 may occur before or after thepartitioning into sub-images 404. To ensure that sub-image scoringmodule 124 can accurately assess sub-images that include part of thewall of well 402, and/or areas outside of well 402, the training datafor the CNN of sub-image scoring module 124 may have included similarsub-images. Alternatively, image pre-processing module 122 may entirelyremove the wall of well 402, and the areas outside well 402, prior tothe analysis performed by the CNN of sub-image scoring module 124.

FIG. 4 also depicts an expanded view of one of sub-images 404. In thedepicted embodiment and scenario, the CNN of sub-image scoring module124 has generated a score of 0.99999 for the expanded-view sub-image404, as a result of a CPE spot in the corresponding portion of well 402.While not shown in FIG. 4 , it is understood that scores for all of theother sub-images 404 may also be determined by sub-image scoring module124. In other embodiments, however, not all of sub-images 404 arescored. In an example embodiment where CPE classification module 126classifies the contents of any given well as “CPE” so long as at leastone of the sub-images in the well is scored over some threshold (e.g.,over 0.90000), for example, then sub-image scoring module 124 may saveprocessing time/power by ceasing to analyze additional sub-images assoon as a first sub-image score for that well exceeds the threshold. Byway of example, the threshold may be greater than or equal to 0.70,0.80, 0.85, 0.90, 0.95, 0.97, 0.98, 0.99, or 0.999 (which may also beexpressed as corresponding percentage probabilities, such as 70%, 85%,90%, 95%, 97%, 98%, 99%, or 99.9%). Any system or method or computerreadable medium as described herein may cease to analyze additionalsub-images as soon as a first sub-image score for that well exceeds thethreshold.

As noted herein, in some embodiments where CPE classification module 126uses an SVM, and the SVM is trained specifically for the cell line beinginspected. In this manner, the classification process performed by theSVM may inherently take into account patterns that are typical for thatcell line. As a relatively simple example, if a first cell linetypically exhibits only smaller, localized CPE spots, while a secondcell line can (with roughly equal probability) exhibit CPE as eithersmall spots or larger, contiguous areas, an SVM for the first cell linemay be more likely to classify the contents of well 402 as “CPE” if thesub-images 404 having high scores collectively form a spot-like pattern,whereas an SVM for the second cell line may not give much (if any)weight to the relative positioning of the sub-images 404 having highscores.

FIG. 5 is a flow diagram of an example method 500 for detecting CPE in awell sample. Method 500 may be implemented by one or more portions ofsystem 100 (e.g., visual inspection system 102 and computer system 104)or another suitable system. As a more specific example, block 502 ofmethod 500 may be implemented by at least a portion of visual inspectionsystem 102 of FIGS. 1 and 2 , while blocks 504 through 510 may beimplemented by computer system 104 (e.g., by processing unit 110 whenexecuting instructions stored in memory unit 114).

At block 502 of method 500, an image of a well containing cells and amedium (and possibly viruses, e.g., according to a controlled dilution)is generated by an imaging unit (e.g., by imager 210 in FIG. 2 ). Themedium may contain cell nutrients, growth factors, etc., and waspreviously inoculated with cells (e.g., cells of a single cell line).

At block 504, the well image is pre-processed, at least by partitioningthe well image into multiple sub-images that each correspond to adifferent portion of the imaged well. In some embodiments, thepre-processing also includes one or more other operations, such asremoving one or more portions of the well image that correspond to oneor more areas outside of the well, for example.

At block 506, for each of the sub-images, a respective score isdetermined using a CNN. Block 506 may occur entirely after block 504, orpartially in parallel with block 504 (e.g., as sub-images aregenerated). The score for each sub-image is indicative of the likelihoodthat any cells in the corresponding portion of the well exhibit CPE. Thescore may be a confidence level associated with a “CPE” classification,for example. The score may be output by the CNN, or a result of somefurther processing of the CNN output and/or other factors. The CNN maybe specific to a particular cell line corresponding to the cells in thewell (e.g., the CNN may have been trained using labeled images of wellscontaining cells of that cell line). In one such embodiment, method 500includes an additional block, occurring sometime prior to block 506, inwhich the appropriate CNN is selected from among multiple CNNsassociated with different cell lines (e.g., based on an input indicatingthe cell line that corresponds to the cells in the well, such as a userinput, or an identifier such as a barcode associated with the well).

At block 508, a CPE status of the cells contained in the well isdetermined based on the scores determined at block 506. The CPE statusmay be a binary indicator of whether the cells exhibit CPE. For exampleblock 508 may include inputting the respective scores to an SVM, whichoutputs the CPE status (e.g., “CPE” or “no CPE”) or a value on which theCPE status is based (e.g., based on one or more additional factors). Inother embodiments, the CPE status is not binary. For example, the CPEstatus may be a probability of the existence of CPE, and/or an extent towhich CPE is (or is likely) exhibited.

At block 510, output data indicating the CPE status determined at block508 is generated. The output data may be displayed to a user on a userinterface of a computing device (e.g., by sending the output data, and acommand that causes display of the output data, to another device ormodule), for example, and/or may be sent to one or more other softwaremodules and/or computer systems for various purposes (e.g., to indicateviral clearance for a particular batch and/or trigger a next phase ofcell line development, etc.).

Although the systems, methods, devices, and components thereof, havebeen described in terms of exemplary embodiments, they are not limitedthereto. The detailed description is to be construed as exemplary onlyand does not describe every possible embodiment of the invention becausedescribing every possible embodiment would be impractical, if notimpossible. Numerous alternative embodiments could be implemented, usingeither current technology or technology developed after the filing dateof this patent that would still fall within the scope of the claimsdefining the invention.

Those skilled in the art will recognize that a wide variety ofmodifications, alterations, and combinations can be made with respect tothe above described embodiments without departing from the scope of theinvention, and that such modifications, alterations, and combinationsare to be viewed as being within the ambit of the inventive concept.

What is claimed is:
 1. A method for detecting cytopathic effect (CPE) ina well sample, the method comprising: generating, by an imaging unit, awell image depicting a well containing cells and a medium;pre-processing, by one or more processors, the well image, whereinpre-processing the well image includes partitioning the well image intoa plurality of sub-images each corresponding to a different portion ofthe well; for each of some or all of the plurality of sub-images,determining, by the one or more processors analyzing the sub-image usinga convolutional neural network, a respective score indicative of alikelihood that any cells in the portion of the well corresponding tothe sub-image exhibit CPE; determining, by the one or more processorsand based on the respective scores for the plurality of sub-images, aCPE status of the cells contained in the well; generating, by the one ormore processors, output data indicating the CPE status; and wherein, ifthe respective score indicative of the likelihood that any cells in theportion of the well corresponding to the sub-image exhibit CPE exceeds aspecified threshold, the one or more processors cease the analysis, andthe CPE status is determined based on the respective score exceeding thespecified threshold.
 2. The method of claim 1, wherein determining theCPE status of the cells contained in the well includes making a binarydetermination of whether the cells contained in the well exhibit CPE. 3.The method of claim 2, wherein making the binary determination ofwhether the cells contained in the well exhibit CPE includes inputtingthe respective scores to a support vector machine (SVM).
 4. The methodof claim 1, wherein: the cells contained in the well are cells of aparticular cell line; and the convolutional neural network is trainedusing labeled images of wells containing cells of the particular cellline.
 5. The method of claim 4, further comprising, prior to determiningthe respective scores: selecting, by the one or more processors andbased on a user input indicating the particular cell line, theconvolutional neural network from among a plurality of convolutionalneural networks each associated with a different cell line.
 6. Themethod of claim 1, wherein pre-processing the well image furtherincludes removing one or more portions of the well image correspondingto one or more areas outside of the well.
 7. The method of claim 1,further comprising: causing, by the one or more processors, a userinterface to display the output data indicating the CPE status to auser.
 8. The method of claim 1, wherein the CPE status of the cellscontained in the well is determined to comprise at least one of: (a) a“not CPE” status; or (b) a probability below a specified threshold thatthe contents of the well image, as a whole, exhibit CPE, the methodfurther comprising transferring a cell of the well to a new cultureenvironment, and culturing the cell in the new culture environment. 9.One or more non-transitory computer-readable media storing instructionsthat, when executed by one or more processors, cause the one or moreprocessors to: pre-process a well image depicting a well containingcells and a medium, at least in part by partitioning the well image intoa plurality of sub-images each corresponding to a different portion ofthe well; for each of some or all of the plurality of sub-images,determine, by analyzing the sub-image using a convolutional neuralnetwork, a respective score indicative of a likelihood that any cells inthe portion of the well corresponding to the sub-image exhibit CPE;determine a CPE status of the cells contained in the well based on therespective scores for the plurality of sub-images; generate output dataindicating the CPE status; and wherein, if the respective scoreindicative of the likelihood that any cells in the portion of the wellcorresponding to the sub-image exhibit CPE exceeds a specifiedthreshold, the instructions cause the one or more processors to ceasethe analysis, and to determine the CPE status based on the respectivescore exceeding the specified threshold.
 10. The one or morenon-transitory computer-readable media of claim 9, wherein theinstructions cause the one or more processors to determine the CPEstatus of the cells contained in the well at least by making a binarydetermination of whether the cells contained in the well exhibit CPE.11. The one or more non-transitory computer-readable media of claim 10,wherein making the binary determination of whether the cells containedin the well exhibit CPE includes inputting the respective scores to asupport vector machine (SVM).
 12. The one or more non-transitorycomputer-readable media of claim 9, wherein: the cells contained in thewell are cells of a particular cell line; and the convolutional neuralnetwork is trained using labeled images of wells containing cells of theparticular cell line.
 13. The one or more non-transitorycomputer-readable media of claim 12, wherein the instructions furthercause the one or more processors to, prior to determining the respectivescores: select, based on a user input indicating the particular cellline, the convolutional neural network from among a plurality ofconvolutional neural networks each associated with a different cellline.
 14. The one or more non-transitory computer-readable media ofclaim 9, wherein the instructions cause the one or more processors topre-process the well image further by removing one or more portions ofthe well image corresponding to one or more areas outside of the well.15. The one or more non-transitory computer-readable media of claim 9,wherein the instructions further cause the one or more processors to:cause a user interface to display the output data indicating the CPEstatus to a user.
 16. A system comprising: a visual inspection systemincluding a stage configured to accept a well plate, and an imaging unitconfigured to generate images of wells within the well plate, whereineach image corresponds to a single well; and a computer system includingone or more processors, and one or more memories storing instructionsthat, when executed by the one or more processors, cause the computersystem to pre-process a well image, generated by the imaging unit anddepicting a well containing cells and a medium, at least in part bypartitioning the well image into a plurality of sub-images eachcorresponding to a different portion of the well, for each of some orall of the plurality of sub-images, determine, by analyzing thesub-image using a convolutional neural network, a respective scoreindicative of a likelihood that any cells in the portion of the wellcorresponding to the sub-image exhibit CPE, determine a CPE status ofthe cells contained in the well based on the respective scores for theplurality of sub-images, generate output data indicating the CPE status;and wherein, if the respective score indicative of the likelihood thatany cells in the portion of the well corresponding to the sub-imageexhibit CPE exceeds a specified threshold, the instructions cause thecomputer system to cease the analysis, and to determine the CPE statusbased on the respective score exceeding the specified threshold.
 17. Thesystem of claim 16, wherein the instructions cause the computer systemto determine the CPE status of the cells contained in the well at leastby making a binary determination of whether the cells contained in thewell exhibit CPE.
 18. The system of claim 17, wherein the instructionscause the computer system to make the binary determination of whetherthe cells contained in the well exhibit CPE at least by inputting therespective scores to a support vector machine (SVM).
 19. The system ofclaim 15, wherein: the cells contained in the well are cells of aparticular cell line; and the convolutional neural network is trainedusing labeled images of wells containing cells of the particular cellline.
 20. The system of claim 19, wherein the instructions further causethe computer system to, prior to determining the respective scores:select, based on an input indicating the particular cell line, theconvolutional neural network from among a plurality of convolutionalneural networks each associated with a different cell line.
 21. Thesystem of claim 16, wherein the computer system further comprises adisplay unit, and wherein the instructions further cause the computersystem to: cause the display unit to display the output data indicatingthe CPE status to a user.